Thanks to this 100% online Postgraduate diploma, you will master the most innovative Artificial Intelligence techniques to obtain Machine Translations defined by their high consistency and accuracy” 

According to a recent report by the United Nations, the implementation of emerging Artificial Intelligence tools has made it possible to optimize accessibility to multilingual content in global development projects by 50%. In this way, it has facilitated understanding between different cultures through cutting-edge methods such as Deep Learning. It is therefore essential for specialists to keep abreast of the most sophisticated Deep Learning and algorithm training techniques to improve Translation in critical sectors such as health, education or Human Rights.

In order to facilitate this update, TECH has created a pioneering Postgraduate diploma in the Application of Artificial Intelligence Techniques for Machine Translation. Designed by references in this field, the academic itinerary will delve into issues ranging from the different probabilistic models of linguistics or emotion detection systems to the generation of autoregressive text. In this way, graduates will obtain advanced competences to design, train and optimize algorithms such as Neural Networks. Furthermore, the teaching materials will delve into the use of state-of-the-art software (including Fluenty, Voice Tra or iTranslate Voice) with the aim of enabling students to perform automatic voice interpretations in special situations that require a common understanding of the language and its use.

Regarding the methodology of the university program, it is taught 100% online so that translation professionals can individually plan their schedules and pace of study. In addition, TECH employs its innovative Relearning method, which consists of the natural and progressive reiteration of the essential concepts of the syllabus to ensure optimal comprehension. In this sense, all students need is an electronic device with an Internet connection to access the Virtual Campus, where they will find various multimedia resources in formats such as interactive summaries, case studies or explanatory videos.

You will learn valuable lessons through real case studies in simulated learning environments”

This Postgraduate diploma in Application of Artificial Intelligence Techniques for Machine Translation contains the most complete and up-to-date educational program on the market. The most important features include: 

• The development of case studies presented by experts in Artificial Intelligence focused on Translation and Interpreting
• The graphic, schematic, and practical contents with which they are created, provide practical information on the disciplines that are essential for professional practice
• Practical exercises where self-assessment can be used to improve learning
• Its special emphasis on innovative methodologies
• Theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments
• Content that is accessible from any fixed or portable device with an Internet connection

Are you looking to implement in your daily practice the latest Artificial Intelligence techniques to automatically translate complex languages such as jargon or technical jargon? Achieve it with this program”

The program’s teaching staff includes professionals from the field who contribute their work experience to this educational program, as well as renowned specialists from leading societies and prestigious universities.

The multimedia content, developed with the latest educational technology, will provide the professional with situated and contextual learning, i.e., a simulated environment that will provide immersive education programmed to learn in real situations.

This program is designed around Problem-Based Learning, whereby the professional must try to solve the different professional practice situations that arise during the course. For this purpose, students will be assisted by an innovative interactive video system created by renowned experts in the field of educational coaching with extensive experience. 

You will learn in depth the use of advanced Computer-Assisted Translation platforms such as Wordbee, which will allow you to carry out quality controls in order to detect common terminological inconsistencies such as spelling mistakes"

With the innovative Relearning methodology applied by TECH, you will consolidate the most complex concepts of the syllabus in a natural and progressive way"

This program has been designed by real experts in Artificial Intelligence applied to Machine Translation. The curriculum will delve into aspects ranging from the implementation of Linguistic Learning models or sentiment analysis systems to different speech recognition methods. In this way, students will develop advanced skills to train and adjust Deep Learning techniques according to different languages and contexts. In addition, the syllabus will analyze the most cutting-edge strategies of Natural Language Processing, which will allow graduates to perform translations of complex grammatical structures in real time and generate fluent texts.

You will handle the most sophisticated algorithms to optimize diverse Machine Translation systems based on Artificial Intelligence, which will allow you to adapt your interpretations to different linguistic contexts”

Module 1. Linguistic Models and Artificial Intelligence Application 

1.1. Classical Models of Linguistics and their Relevance to Artificial Intelligence

1.1.1. Generative and Transformational Grammar
1.1.2. Structural Linguistic Theory
1.1.3. Formal Grammar Theory
1.1.4. Applications of Classical Models in Artificial Intelligence

1.2. Probabilistic Models in Linguistics and Their Application in Artificial Intelligence

1.2.1. Hidden Markov Models (HMM)
1.2.2. Statistical Language Models
1.2.3. Supervised and Unsupervised Learning Algorithms
1.2.4. Applications in Speech Recognition and Text Processing

1.3. Rule-Based Models and Their Implementation in AI. GPT

1.3.1. Formal Grammars and Rule Systems
1.3.2. Knowledge Representation and Computational Logic
1.3.3. Expert Systems and Inference Engines
1.3.4. Applications in Dialog Systems and Virtual Assistants

1.4. Deep Learning Models in Linguistics and Their Use in Artificial Intelligence

1.4.1. Convolutional Neural Networks for Text Processing
1.4.2. Recurrent Neural Networks and LSTM for Sequence Modeling
1.4.3. Attention Models and Transformers. APERTIUM
1.4.4. Applications in Machine Translation, Text Generation and Sentiment Analysis

1.5. Distributed Language Representations and Their Impact on Artificial Intelligence

1.5.1. Word Embeddings and Vector Space Models
1.5.2. Distributed Representations of Sentences and Documents
1.5.3. Bag-of-Words Models and Continuous Language Models
1.5.4. Applications in Information Retrieval, Document Clustering and Content Recommendation

1.6. Machine Translation Models and Their Evolution in AI. Lilt

1.6.1. Statistical and Rule-Based Translation Models
1.6.2. Advances in Neural Machine Translation
1.6.3. Hybrid Approaches and Multilingual Models
1.6.4. Applications in Online Translation and Content Localization Services

1.7. Sentiment Analysis Models and Their Usefulness in Artificial Intelligence

1.7.1. Sentiment Classification Methods
1.7.2. Detection of Emotions in Text
1.7.3. Analysis of User Opinions and Comments
1.7.4. Applications in Social Networks, Analysis of Product Opinions and Customer Service

1.8. Language Generation Models and Their Application in AI. TransPerfect Globallink

1.8.1. Autoregressive Text Generation Models
1.8.2. Conditioned and Controlled Text Generation
1.8.3. GPT-Based Natural Language Generation Models
1.8.4. Applications in Automatic Typing, Text Summarization, and Intelligent Conversation

1.9. Speech Recognition Models and Their Integration in Artificial Intelligence

1.9.1. Audio Feature Extraction Methods
1.9.2. Speech Recognition Models Based on Neural Networks
1.9.3. Improvements in Speech Recognition Accuracy and Robustness
1.9.4. Applications in Virtual Assistants, Transcription Systems and Speech-based Device Control

1.10. Challenges and Future of Linguistic Models in Artificial Intelligence

1.10.1. Challenges in Natural Language Understanding
1.10.2. Limitations and Biases in Current Linguistic Models
1.10.3. Research and Future Trends in Artificial Intelligence Linguistic Modeling
1.10.4. Impact on Future Applications such as General Artificial Intelligence (AGI) and Human Language Understanding. SmartCAt

Module 2. Artificial Intelligence and Real-Time Translation

2.1. Introduction to Real-Time Translation with Artificial Intelligence

2.1.1. Definition and Basic Concepts
2.1.2. Importance and Applications in Different Contexts
2.1.3. Challenges and Opportunities
2.1.4. Tools such as Fluently or Voice Tra

2.2. Artificial Intelligence Fundamentals in Translation

2.2.1. Brief Introduction to Artificial Intelligence
2.2.2. Specific Applications in Translation
2.2.3. Relevant Models and Algorithms

2.3. Artificial Intelligence-Based Real-Time Translation Tools

2.3.1. Description of the Main Tools Available
2.3.2. Comparison of Functionalities and Features
2.3.3. Use Cases and Practical Examples

2.4. Neural Machine Translation (NMT) Models. SDL Language Cloud

2.4.1. Principles and Operation of NMT Models
2.4.2. Advantages over Traditional Approaches
2.4.3. Development and Evolution of NMT Models

2.5. Natural Language Processing (NLP) in Real-Time Translation. SayHi TRanslate

2.5.1. Basic NLP Concepts Relevant to Translation
2.5.2. Preprocessing and Post-Processing Techniques
2.5.3. Improving the Coherence and Cohesion of the Translated Text

2.6. Multilingual and Multimodal Translation Models

2.6.1. Translation Models that Support Multiple Languages
2.6.2. Integration of Modalities such as Text, Speech and Images
2.6.3. Challenges and Considerations in Multilingual and Multimodal Translation

2.7. Quality Assessment in Real-Time Translation with Artificial Intelligence

2.7.1. Translation Quality Assessment Metrics
2.7.2. Automatic and Human Evaluation Methods. iTranslate Voice
2.7.3. Strategies to Improve Translation Quality

2.8. Integration of Real-Time Translation Tools in Professional Environments

2.8.1. Use of Translation Tools in Daily Work
2.8.2. Integration with Content Management and Localization Systems
2.8.3. Adaptation of Tools to Specific User Needs

2.9. Ethical and Social Challenges in Real-Time Translation with Artificial Intelligence

2.9.1. Biases and Discrimination in Machine Translation
2.9.2. Privacy and Security of User Data
2.9.3. Impact on Linguistic and Cultural Diversity

2.10. Future of AI-Based Real-Time Translation. Applingua

2.10.1. Emerging Trends and Technological Advances
2.10.2. Future Prospects and Potential Innovative Applications
2.10.3. Implications for Global Communication and Language Accessibility

Module 3. Artificial Intelligence-Assisted Translation Tools and Platforms

3.1. Introduction to Artificial Intelligence-Assisted Translation Tools and Platforms

3.1.1. Definition and Basic Concepts
3.1.2. Brief History and Evolution
3.1.3. Importance and Benefits in Professional Translation

3.2. Main Artificial Intelligence-Assisted Translation Tools

3.2.1. Description and Functionalities of the Leading Tools on the Market
3.2.2. Comparison of Features and Prices
3.2.3. Use Cases and Practical Examples

3.3. Professional AI-Assisted Translation Platforms. Wordfast

3.3.1. Description of Popular Artificial Intelligence-Assisted Translation Platforms
3.3.2. Specific Functionalities for Translation Teams and Agencies
3.3.3. Integration with Other Project Management Systems and Tools

3.4. Machine Translation Models Implemented in AI-Assisted Translation Tools

3.4.1. Statistical Translation Models
3.4.2. Neural Translation Models
3.4.3. Advances in Neural Machine Translation (NMT) and Its Impact on AI-Assisted Translation Tools

3.5. Integration of Linguistic Resources and Databases in AI-Assisted Translation Tools

3.5.1. Using Corpus and Linguistic Databases to Improve Translation Accuracy
3.5.2. Integrating Specialized Dictionaries and Glossaries
3.5.3. Importance of Context and Specific Terminology in Artificial Intelligence-Assisted Translation

3.6. User Interface and User Experience in AI-Assisted Translation Tools

3.6.1. User Interface Design and Usability
3.6.2. Customization and Preference Settings
3.6.3. Accessibility and Multilingual Support on AI-Assisted Translation Platforms

3.7. Quality Assessment in Artificial Intelligence-Assisted Translation

3.7.1. Translation Quality Assessment Metrics
3.7.2. Machine vs. Human Evaluation
3.7.3. Strategies to Improve the Quality of Artificial Intelligence-Assisted Translation

3.8. Integration of AI-Assisted Translation Tools into the Translator's Workflow

3.8.1. Incorporation of AI-Assisted Translation Tools into the Translation Process
3.8.2. Optimizing Workflow and Increasing Productivity
3.8.3. Collaboration and Teamwork in Artificial Intelligence-Assisted Translation Environments

3.9. Ethical and Social Challenges in the Use of AI-Assisted Translation Tools

3.9.1. Biases and Discrimination in Machine Translation
3.9.2. Privacy and Security of User Data
3.9.3. Impact on the Translation Profession and on Linguistic and Cultural Diversity

3.10. Future of AI-Assisted Translation Tools and IA. Wordbee

3.10.1. Emerging Trends and Technological Developments
3.10.2. Future Prospects and Potential Innovative Applications
3.10.3. Implications for Training and Professional Development in the Field of Translation

A unique training experience, key and decisive to boost your professional development"